With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. There has been a growing interest in approaching the problem of quantum state reconstruction using generative neural network models. Here we propose the ‘attention-based quantum tomography’ (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state.

The phase engineering of transition metal dichalcogenides (TMDs) is considered a promising strategy for promoting efficient catalysis, such as the hydrogen evolution reaction (HER). While theoretical studies predict the presence of catalytically active atomic sites at heterophase boundaries in TMDs, conventional bulk HER measurements are not able to precisely explore these 1D heterophase regions for HER. Here, one reports on active HER occurring at heterophase boundaries between the semiconducting 2H and metallic 1T’ phases in large-scale MoTe2 grown via chemical vapor deposition.

Conventional gating in transistors uses electric fields through external dielectrics that require complex fabrication processes. Various optoelectronic devices deploy photogating by electric fields from trapped charges in neighbor nanoparticles or dielectrics under light illumination. Orbital gating driven by giant Stark effect is demonstrated in tunneling phototransistors based on 2H-MoTe2 without using external gating bias or slow charge trapping dynamics in photogating.

Hemophilia is a hereditary disease that remains incurable. Although innovative treatments such as gene therapy or bispecific antibody therapy have been introduced, substantial unmet needs still exist with respect to achieving long-lasting therapeutic effects and treatment options for inhibitor patients. Antithrombin (AT), an endogenous negative regulator of thrombin generation, is a potent genome editing target for sustainable treatment of patients with hemophilia A and B.

Even as the understanding of the mechanism behind correlated insulating states in magic-angle twisted bilayer graphene converges toward various kinds of spontaneous symmetry breaking, the metallic "normal state"above the insulating transition temperature remains mysterious, with its excessively high entropy and linear-in-temperature resistivity. In this Letter, we focus on the effects of fluctuations of the order parameters describing correlated insulating states at integer fillings of the low-energy flat bands on charge transport.

Lung-resident memory T cells (TRM) play an essential role in protecting against pulmonary virus infection. Parenteral administration of DNA vaccine is generally not sufficient to induce lung CD8 TRM cells. This study investigates whether intramuscularly administered DNA vaccine expressing the nucleoprotein (NP) induces lung TRM cells and protects against the influenza B virus. The results show that DNA vaccination poorly generates lung TRM cells and massive secondary effector CD8 T cells entering the lungs after challenge infection do not offer sufficient protection.

Non-Fermi liquid physics is ubiquitous in strongly correlated metals, manifesting itself in anomalous transport properties, such as a T-linear resistivity in experiments. However, its theoretical understanding in terms of microscopic models is lacking, despite decades of conceptual work and attempted numerical simulations.

Image-like data from quantum systems promises to offer greater insight into the physics of correlated quantum matter. However, the traditional framework of condensed matter physics lacks principled approaches for analyzing such data. Machine learning models are a powerful theoretical tool for analyzing image-like data including many-body snapshots from quantum simulators. Recently, they have successfully distinguished between simulated snapshots that are indistinguishable from one and two point correlation functions.

Despite their fundamental importance in dictating the quantum-mechanical properties of a system, ground states of many-body local quantum Hamiltonians form a set of measure zero in the many-body Hilbert space. Hence determining whether a given many-body quantum state is ground-stateable is a challenging task. Here we propose an unsupervised machine learning approach, dubbed Entanglement Clustering ("EntanCl"), to separate out ground-stateable wave functions from those that must be excited-state wave functions using entanglement structure information.

The tuning catalytic functionality of transition metal dichalcogenides (TMDs) with multi-dimensional defects, such as interfaces (2D), edges (1D), and atomic vacancies (0D), is currently considered a promising strategy for energy applications. The pristine edges and plasma-treated basal planes of various TMDs have been extensively studied for practical hydrogen evolution reaction (HER). Here, we demonstrate active HER on the plasma-treated edges of semimetallic layered tungsten ditellurides (WTe2) using a microcell device.